Computer networks and systems have become indispensable tools for modern business. Today terabits of information on virtually every subject imaginable are stored in and accessed across such networks by users throughout the world. Much of this information is, to some degree, confidential and its protection is required. Not surprisingly then, intrusion detection systems (IDS) have been developed to help uncover attempts by unauthorized persons and/or devices to gain access to computer networks and the information stored therein.
Intrusion detection may be regarded as the art of detecting inappropriate, incorrect or anomalous activity within or concerning a computer network or system. The most common approaches to intrusion detection are statistical anomaly detection and pattern-matching detection. IDS that operate on a host to detect malicious activity on that host are called host-based IDS (and may exist in the form of host wrappers/personal firewalls or agent-based software), and those that operate on network data flows are called network-based IDS. Host-based intrusion detection involves loading software on the system (the host) to be monitored and using log files and/or the host's auditing agents as sources of data. In contrast, a network-based intrusion detection system monitors the traffic on its network segment and uses that traffic as a data source. Packets captured by the network interface cards are considered to be of interest if they match a signature.
Regardless of the data source, there are two complementary approaches to detecting intrusions: knowledge-based approaches and behavior-based approaches. Almost all IDS tools in use today are knowledge-based. Knowledge-based intrusion detection techniques involve comparing the captured data to information regarding known techniques to exploit vulnerabilities. When a match is detected, an alarm is triggered. Behavior-based intrusion detection techniques, on the other hand, attempt to spot intrusions by observing deviations from normal or expected behaviors of the system or the users (models of which are extracted from reference information collected by various means). When a suspected deviation is observed, an alarm is generated.
Advantages of the knowledge-based approaches are that they have the potential for very low false alarm rates, and the contextual analysis proposed by the intrusion detection system is detailed, making it easier for a security officer using such an intrusion detection system to take preventive or corrective action. Drawbacks include the difficulty in gathering the required information on the known attacks and keeping it up to date with new vulnerabilities and environments.
Advantages of behavior-based approaches are that they can detect attempts to exploit new and unforeseen vulnerabilities. They are also less dependent on system specifics. However, the high false alarm rate is generally cited as a significant drawback of these techniques and because behaviors can change over time, the incidence of such false alarms can increase.
With both knowledge-based and behavior-based systems, matches are detected with the aid of a rules engine. Many current rules engines implement a standard RETE algorithm because the rules engine's performance is demonstrably independent of the number of rules that are used.
Regardless of whether a host-based or a network-based implementation is adopted and whether that implementation is knowledge-based or behavior-based, an intrusion detection system is only as useful as its ability to discriminate between normal system usage and true intrusions (accompanied by appropriate alerts). If intrusions can be detected and the appropriate personnel notified in a prompt fashion, measures can be taken to avoid compromises to the protected system. Otherwise such safeguarding cannot be provided. Accordingly, what is needed is a system that can provide accurate and timely intrusion detection and alert generation so as to effectively combat attempts to compromise a computer network or system